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Abstract
Building change detection makes it is easy to locate buildings from a distance in the sky. They can also observe the development of rural, or urban areas between 10 decade and present. So, higher resolution satellite and aerial pictures are needed to detect buildings. Building shape varies from one to another over the world. Rural areas are sparsely populated, but densely and complexly populated in urban areas. And it is difficult to detect separate buildings from them. To solve obstacles, non-linear filter, line extracting and region thresholding method is used in this research. The test images from the last decade and images of current year are acquired by using google earth pro, and have different spatial resolutions. Detection area is Hlaingthaya Township, Yangon, Myanmar. This system is simulated with MATLAB programming language.
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References
K. Segl and H. Kaufmann, “Detection of small objects from highresolution panchromatic satellite imagery based on supervised image segmentation,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 9, pp. 2080–2083, Sep. 2001.
https://ieeexplore.ieee.org/document/951105
M. Molinier, J. Laaksonen, and T. Hame, “Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on self-organizing maps,” IEEE Trans. Geosci. Remote Sens.,vol. 45, no. 4, pp. 861–874, Apr. 2007.
https://cris.vtt.fi/en/publications/detecting-man-made-structures-and-changes-in-satellite-imagery-wi
P. Gamba, F. D. Acqua, G. Lisini, and G. Trianni, “Improved VHR urban area mapping exploiting object boundaries,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 8, pp. 2676–2682, Aug. 2007.
https://ieeexplore.ieee.org/document/5523977
J. A. Benediktsson, M. Pesaresi, and K. Arnason, “Classification and
feature extraction for remote sensing images from urban areas based on morphological transformations,” IEEE Trans. Geosci. Remote Sens.,vol. 41, no. 9, pp. 1940–1949, Sep. 2003.
https://www.researchgate.net/publication/261241425_Hedges_detection_using_local_directional_features_and_support_vector_data_description
L. M. Fonte, S. Gautama,W. Philips, andW. Goeman, “Evaluating corner detectors for the extraction of manmade structures in urban areas,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2005, pp. 237–240.
https://biblio.ugent.be/publication/406046
E. Rosten, R. Porter, and T. Drummond, “Faster and better: A machine
learning approach to corner detection,” IEEE Trans. Pattern Anal.Mach.Learn., vol. 32, no. 1, pp. 105–119, Nov. 2010.
https://ieeexplore.ieee.org/document/4674368
C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point
detectors,” Int. J. Comput. Vis., vol. 37, no. 2, pp. 151–172, Jun. 2000.
https://hal.inria.fr/inria-00548302/document
C. Harris and M. Stephens, “A combined corner and edge detector,” in
Proc. 4th Alvey Vis. Conf., 1988, pp. 147–151
https://www.semanticscholar.org/paper/A-Combined-Corner-and Edge-Detector-Harris-Stephens/